Positron emission tomography (PET) using glucose analog [18F]2-fluoro-D-2-deoxyglucose (FDG) has been developed essentially as a diagnostic tool for neoplasms. Recently, FDG-PET has been used for staging, treatment response, restaging after therapy and prognosis for lymphoma, lung cancer, head and neck tumors, thyroid carcinoma, breast cancer, and many other malignances. Besides its use as a diagnostic tool in oncology, FDG-PET is increasingly used in target volume definition as a planning tool for radiotherapy. The role of FDG-PET in radiation therapy treatment planning has been investigated for several malignancies including lung, head and neck, brain, cervix and other tumor sites. FDG-PET imaging has had a great impact on the gross tumor volume (GTV) definition, especially for lung cancer. Unfortunately, in recent literatures, there is no general agreement about a uniformly applicable method for accurate target volume delineation.
The accuracy of GTV definition is essential in conformal radiation therapy such as intensity modulated radiation therapy (IMRT). Conventionally, GTV was based on volume data derived from CT scanning. However, CT has relatively low contrast for soft tissue which makes it difficult to differentiate the malignancy when the tumor has similar electron density with normal tissue. Previous investigation suggests that FDG-PET has the potential to provide more accurate GTV definition and reduce inter-observer variability. The research shows FDG-PET based GTV definition is superior to those by CT alone for a moving target. However, comparing to CT or MRI images, FDG-PET images have low spatial resolution, high partial volume effect and insufficient anatomical details which result in difficulty to define the exact border of tumor. More attention and efforts are considered necessary to incorporate PET functional imaging information into radiation therapy treatment planning.
Currently, thresholding segmentation is the most widely used automatic method for PET target delineation in research and clinical application although other techniques are also under investigation. A relatively simple thresholding method using fixed threshold of 40% of maximal image intensity or standardized uptake value (SUV) of 2.5 as a cut off factor is still employed in clinical application. Many recent investigations indicate that the fixed thresholding or absolute value thresholding is of limited accuracy, especially for targets with small volume or low contrast. The optimal threshold which can best define the actual target volume depends on source/background image intensity ratio (S/B ratio) and target volume.
Accordingly, an adaptive thresholding method was developed using a computer tomography (CT) volume as the initial estimate of target volume. In this method, a family of exponential threshold-volume curves for different S/B ratios was obtained from fitting data of an initial sphere phantom study. Depending on measured target S/B ratio, the given CT volume was applied to the corresponding curve to yield the desired threshold for target delineation. Based on the same hypothesis, an iterative method was developed recently. Instead of using CT volume as a prior knowledge, the threshold deriving from fitted curves and thresholding segmentation procedures were performed iteratively until the yielded threshold would not change. Another local contrast based method employed similar iterative technique slice-by-slice to obtain the desired threshold for each slice instead of one global threshold for the whole volume. The adaptive thresholding method had good performance if given the target CT volume which is not always available in clinic. The iterative methods do not need the CT volume as initial estimate of target volume. However the convergence of the iterative procedure is questionable when applied to a target volume smaller than 4 mL. Moreover, these methods confuse the S/B ratio with physical FDG concentration ratio of target and background. Unlike the above techniques, Black's mean SUV method employed a linear relationship between threshold SUV and mean target SUV iteratively to yield desired threshold SUV. Because the SUV are machine-specific and patient-specific values, this linear relationship needs to be modified to accommodate different scanners.